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一种与蛋白质翻译后修饰相关的新型基因标志物,用于预测结直肠癌的临床结局和治疗反应。

A Novel Gene Signature Associated with Protein Post-translational Modification to Predict Clinical Outcomes and Therapeutic Responses of Colorectal Cancer.

机构信息

Department of Gastrointestinal Surgery, The Second Affiliated Hospital of Chongqing Medical University, 74 Linjiang Road, Yuzhong District, Chongqing, 400010, China.

出版信息

Mol Biotechnol. 2024 Aug;66(8):2106-2122. doi: 10.1007/s12033-023-00852-6. Epub 2023 Aug 17.

Abstract

Accumulated evidence highlights the biological significance of diverse protein post-translational modifications (PTMs) in tumorigenicity and progression of colorectal cancer (CRC). In this study, ten PTM patterns (ubiquitination, methylation, phosphorylation, glycosylation, acetylation, SUMOylation, citrullination, neddylation, palmitoylation, and ADP-ribosylation) were analyzed for model construction. A post-translational modification index (PTMI) with a 14-gene signature was established. CRC patients with high PTMI had a worse prognosis after validating in nine independent datasets. By incorporating PTMI with clinical features, a nomogram with excellent predictive performance was constructed. Two molecular subtypes of CRC with obvious difference in survival time were identified by unsupervised clustering. Furthermore, PTMI was related to known immunoregulators and key tumor microenvironment components. Low-PTMI patients responded better to fluorouracil-based chemotherapy and immune checkpoint blockade therapy compared to high-PTMI patients, which was validated in multiple independent datasets. However, patients with high PTMI might be sensitive to bevacizumab. In short, we established a novel PTMI model by comprehensively analyzing diverse post-translational modification patterns, which can accurately predict clinical prognosis and treatment response of CRC patients.

摘要

积累的证据强调了蛋白质翻译后修饰(PTMs)在结直肠癌(CRC)的致癌性和进展中的生物学意义。在这项研究中,分析了十种 PTM 模式(泛素化、甲基化、磷酸化、糖基化、乙酰化、SUMO 化、瓜氨酸化、类泛素化、棕榈酰化和 ADP-核糖基化)用于模型构建。建立了一个具有 14 个基因特征的翻译后修饰指数(PTMI)。在九个独立数据集验证后,PTMI 高的 CRC 患者预后更差。通过将 PTMI 与临床特征相结合,构建了一个具有优异预测性能的列线图。通过无监督聚类鉴定出两种具有明显生存时间差异的 CRC 分子亚型。此外,PTMI 与已知的免疫调节剂和关键肿瘤微环境成分有关。与高 PTMI 患者相比,低 PTMI 患者对基于氟尿嘧啶的化疗和免疫检查点阻断治疗的反应更好,这在多个独立数据集得到了验证。然而,高 PTMI 患者可能对贝伐单抗敏感。总之,我们通过综合分析多种翻译后修饰模式建立了一个新的 PTMI 模型,该模型可以准确预测 CRC 患者的临床预后和治疗反应。

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